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Estimate Approximate Entropy

Interactively estimate the approximate entropy of a uniformly sampled signal in the Live Editor

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Description

The Estimate Approximate Entropy task lets you interactively estimate the approximate entropy of a uniformly sampled signal. The task automatically generates MATLAB® code for your live script. For more information about Live Editor tasks generally, see Add Interactive Tasks to a Live Script.

Approximate entropy is a regularity statistic that quantifies the unpredictability of fluctuations in a time series. A relatively higher value of approximate entropy reflects the likelihood that similar patterns of observations are not followed by additional similar observations.

Estimate Approximate Entropy task in Live Editor

Open the Task

To add the Estimate Approximate Entropy task to a live script in the MATLAB Editor:

  • On the Live Editor tab, select Task > Estimate Approximate Entropy.

  • In a code block in your script, type a relevant keyword, such as approximate or approximate entropy. Select Estimate Approximate Entropy from the suggested command completions.

Examples

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Open Live Script

Use the Estimate Approximate Entropy task in the Live Editor to interactively estimate the approximate entropy of a uniformly sampled signal. Experiment with different values for lag, embedding dimension and radius. The task automatically generates code reflecting your selections. Open this example to see a preconfigured script containing the Estimate Approximate Entropy task.

For this example, consider 'approxEntData.mat' which contains uniformly sampled signal X, embedding dimension dim, and the time delay lag.

load('approxEntData.mat','X','dim','lag')

To approximate entropy of the signal X, open the Estimate Approximate Entropy task in the Live Editor. On the Live Editor tab, select Task > Estimate Approximate Entropy. In the task, select signal X.

Select dim for the Embedding Dimension and lag for the Time Lag dropdown menu respectively. If you do not know the embedding dimension and the time lag for your signal, use the Reconstruct Phase Space task to compute the values.

Evaluate whether the value of approximate entropy is affected drastically by changing the Radius field and observe the change in value in the Live Editor output. You can toggle displaying the output of the approximate entropy value in the Live Editor output using the Output Display option.

The task generates code in your live script. The generated code reflects the parameters and options you specify. To see the generated code, click at the bottom of the task parameter area. The task expands to display the generated code.

By default, the generated code uses approxEnt as the name of the output variable. To specify a different output variable name, enter a new name in the summary line at the top of the task. For instance, change the name to aEntropy.

The task updates the generated code to reflect the new variable name, and the new variable aEntropy appears in the MATLAB workspace.

Parameters

Select Signal

Select a uniformly sampled time-domain signal in array or timetable format. If the signal has multiple columns, the Estimate Approximate Entropy task computes the approximate entropy by treating it as a multivariate signal. If the signal is a row vector, then the Estimate Approximate Entropy task treats it as a univariate signal.

Specify Approximate Entropy Parameters

Specify the number of dimensions of phase space vectors as a scalar or vector from the MATLAB workspace. When you specify the embedding dimension as a scalar, then every column of the uniformly sampled signal is computed using the same embedding dimension value.

If you do not know the value of embedding dimension for your signal, then you can compute it using the Reconstruct Phase Space task.

Specify time lag between successive phase vectors as a scalar or vector from the MATLAB workspace. When you specify the time lag as a scalar, then the Estimate Approximate Entropy task uses the same time delay value to estimate the value of approximate entropy for all the columns of the uniformly sampled signal. If you specify the embedding dimension as a vector, then specify the time lag also as a vector of the same length.

If you do not know the value of time lag for your signal, then you can compute it using the Reconstruct Phase Space task.

Specify similarity criterion as a scalar. The similarity criterion, also called radius of similarity, is a tuning parameter that is used to identify a meaningful range in which fluctuations in data are to be considered similar.

Visualize Results

Toggle to display the value of approximate entropy in the Live Editor output.

Version History

Introduced in R2019b

See Also

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